论文标题
线性混合布尔算术表达式的有效去量
Efficient Deobfuscation of Linear Mixed Boolean-Arithmetic Expressions
论文作者
论文摘要
混合的布尔值（MBA）表达式经常用于混淆。由于它们结合了算术和布尔操作，因此算术规律和逻辑公式的转换规则都不能应用于适当复杂的表达式，从而使MBA难以简化和解决。在2019年，Liu等人。揭示线性MBA，利用位值的集合$ b = \ {0,1 \} $与Lineare Mbas的长度$ n \ in \ Mathbb {n} $ in \ Mathbb {n} $的集合$ b^n $，最初由Zhou等人引入。在2007年。凭借其MBA-Blast和MBA-Solver算法，它们在性能以及简化此类MBA的能力方面的表现明显优于现有工具。我们提出了一种令人惊讶的简单算法，称为Simba，它可以改善MBA-Blast和MBA-Solver，因为它可以消除所有线性MBA，并不会错过特别简单的解决方案，并且仅占其运行时的一小部分。
Mixed Boolean-Arithmetic (MBA) expressions are frequently used for obfuscation. As they combine arithmetic as well as Boolean operations, neither arithmetic laws nor transformation rules for logical formulas can be applied to suitably complex expressions, making MBAs hard to simplify and solve. In 2019, Liu et al. demystified linear MBAs, leveraging a transformation between the set $B=\{0,1\}$ of bit values and the set $B^n$ of words of length $n\in\mathbb{N}$ for linear MBAs, originally introduced by Zhou et al. in 2007. With their MBA-Blast and MBA-Solver algorithms, they outperform existing tools noticably in terms of performance as well as ability to simplify of such MBAs. We propose a surprisingly simple algorithm called SiMBA that improves upon MBA-Blast and MBA-Solver in that it can deobfuscate all linear MBAs, does not miss particularly simple solutions and takes only a fraction of their runtime.
